Data-driven modeling and fault diagnosis for fuel cell vehicles using deep learning

IF 9.6 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Energy and AI Pub Date : 2024-01-24 DOI:10.1016/j.egyai.2024.100345
Yangeng Chen , Jingjing Zhang , Shuang Zhai , Zhe Hu
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Abstract

The reliability and safety of fuel cell vehicle are crucial for the daily operation. Insulation resistance serves as a crucial index of vehicle reliability, especially when fuel cells operate at high voltages. Low insulation resistance can lead to vehicle malfunctions, exposing the operator to the risk of electric shock. In this study, long-term insulation resistance data from thirteen vehicles equipped with three different types of fuel cell systems are analyzed to diagnose possible low insulation resistance issues. For this purpose, a robust locally weighted scatterplot smoothing method is utilized to filter the original data. In this research, an insulation variation model is developed using a data-driven long short-term memory neural network to identify insulation resistance value anomalies caused by deionizer failure. The results indicate that the coefficient of determination of the failure model is 99.78 %. Moreover, current model efficiently identifies insulation faults resulting from reliability issues, such as conductivity issues of cooling pipes and erosion of vehicle wiring harnesses.

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利用深度学习对燃料电池汽车进行数据驱动建模和故障诊断
燃料电池汽车的可靠性和安全性对日常运行至关重要。绝缘电阻是衡量车辆可靠性的重要指标,尤其是当燃料电池在高电压下工作时。绝缘电阻过低会导致车辆故障,使操作人员面临触电风险。本研究分析了 13 辆配备三种不同类型燃料电池系统的车辆的长期绝缘电阻数据,以诊断可能存在的低绝缘电阻问题。为此,采用了一种稳健的局部加权散点图平滑方法来过滤原始数据。在这项研究中,利用数据驱动的长短期记忆神经网络开发了一个绝缘变化模型,以识别由去离子器故障引起的绝缘电阻值异常。结果表明,故障模型的判定系数为 99.78 %。此外,当前模型还能有效识别可靠性问题导致的绝缘故障,如冷却管的导电性问题和汽车线束的侵蚀问题。
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来源期刊
Energy and AI
Energy and AI Engineering-Engineering (miscellaneous)
CiteScore
16.50
自引率
0.00%
发文量
64
审稿时长
56 days
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